Abandoned object detection is one of the most desired video analytics applications for public safety and security. Abandoned objects may contain explosives or other harmful agents. Typically, the application makes an initial determination as to potential abandoned objects and then a human makes the final decision. In urban surveillance at a city level, it is not uncommon to have hundreds of cameras monitoring public places such as streets, roads and buildings. For such large-scale visual analysis by computer, one widely acknowledged issue is the substantial number of false alarms, which can make human adjudication a very daunting task. In realistic environments, many things can be falsely detected as abandoned objects. Among them, lighting artifacts are a dominant source of false positives.
Lighting artifacts, which manifest as brighter or darker areas on surfaces of objects, relative to a “background” image, can be caused by many factors, and can vary during the course of a day, such as due to the transit of the sun across the sky, the transition from night to day, changing from natural lighting to artificial illumination, changes in natural lighting due to changing weather conditions, changes to the reflectivity of surfaces due to rain, turning on or off artificial illumination, and headlights on vehicles, including trains, airplanes, and other mass transit vehicles. In addition, artifacts caused by the transit of the sun can change throughout the year as a result of the precession of the Earth's axis relative to the sun. This movement of the lighting artifacts make them difficult to classify as background objects which are typically fixed in location over a certain period of time. In abandoned object detection applications, lighting artifacts often produce false positives because the artifacts can appear so quickly that the background modeling doesn't have time to adapt to the change in lighting and include the artifact as part of the background.